Deep venous thrombosis (DVT) and its sequelae are frequent complications in hospitalized patients and are difficult to diagnose. Patients with DVT may have symptoms and signs which are nonspecific or may be asymptomatic. Contrast venography is the "gold standard" for the detection of venous thrombosis, but it has several features which limit its utility. The hypothesis in this proposal is that DVT can be accurately detected using magnetic resonance imaging (MRI), impedance plethysmography (IPG), ultrasound (US) and/or indium-111 platelet studies. MRI gives information concerning the thrombus and its effect on flow. IPG gives information related to venous capacitance and venous outflow. US demonstrates DVT by non-compressibility of the vein lumen and the effects on flow. In-111 platelet imaging demonstrates sites of active platelet deposition. These modalities will be compared to venography in 2 groups of patients. Group I consists of patients with gynecologic malignancy and a greater than 30% risk of having DVT in the perioperative period. These patients will be studied with In-111 platelets, MRI, IPG, US and contrast venography. The labeled platelets will be administered 24 hours after surgery and images will be made daily. The other studies will be performed on postoperative day 5 or earlier if the In-111 platelet scan is abnormal. Group II consists of patients referred for contrast venography for suspected DVT. These patients will have In-111 platelet imaging, MRI, IPG, US and contrast venography performed. In patients with DVT, the MRI study will characterize the change in the thrombus and its effect on flow and IPG will characterize the change in impedance for 6 weeks. The ability of MRI and In-111 platelet imaging to quantitate the size of thrombus will be compared to contrast venography. The diagnostic accuracy of In-111 platelets, MRI, IPG, and US compared to venography will be determined. The long-term objective of this proposal is to develop an algorithm in these patient groups for the appropriate evaluation of possible DVT. It is anticipated that an algorithm can be developed which will accurately detect DVT without the use of contrast venography.